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Record W4285018865 · doi:10.1177/08404704221109760

Lessons learned about MAiD from a Catholic healthcare perspective

2022· article· en· W4285018865 on OpenAlexaffabout
Gordon Self

Bibliographic record

VenueHealthcare Management Forum · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCovenant Health
Fundersnot available
KeywordsCovenantGovernment (linguistics)Perspective (graphical)Health carePublic relationsQuality (philosophy)Identity (music)NursingBusinessPublic administrationPolitical scienceLawSociologyMedicine

Abstract

fetched live from OpenAlex

As a Catholic healthcare organization, Covenant Health and the Covenant family of institutions in Alberta does not provide Medical Assistance in Dying (MAiD). However, given its significant palliative and hospice bed base, it responds on average once per week to persons in their care requesting MAiD, requiring Covenant to balance the rights of individuals exploring this legally available option without compromising on its institutional identity and ethical integrity. The article details Covenant's advocacy role with government and public messaging, demonstrating how balancing the rights of all is not only necessary, but possible. The article provides recommendations for both provider and non-provider sites alike to ensure appropriate safeguards, quality control, and longitudinal study, given the unprecedented impact MAiD has had on the national landscape, underscoring there are lessons learned for all to benefit.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.021
Scholarly communication0.0090.011
Open science0.0020.011
Research integrity0.0100.026
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.181
GPT teacher head0.468
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes2
Has abstractyes

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